30 Agents Every AI Engineer Must Build: Build production-ready agent systems using proven architectures and patterns

Learn to transform LLM capabilities into production-ready agent systems using practical patterns and domain-driven approaches, guided by Imran Ahmad, author of 50 Algorithms Every Programmer Should Know

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Key Features Design and implement 30 proven agent architectures used in real-world production environments Build scalable, secure, and resilient agent workflows that move beyond simple chat interfaces Master core agentic principles—perception, memory, reasoning, and planning—to create truly autonomous systems Book Description As AI evolves from passive tools into proactive collaborators, intelligent agents are leading a fundamental shift in computing. This guide provides the critical knowledge of agent architectures, practical tools, and industry approaches needed to build robust, autonomous AI systems that do more than just generate text—they act.

You will begin by mastering foundational capabilities: perception, memory, reasoning, planning, and learning. You’ll gain deep insight into the cognitive loops that drive autonomous behavior and build sophisticated architectures using frameworks such as LangChain and LangGraph.

The book explores high-impact applications across diverse sectors, including software development, finance, manufacturing, legal and education, to show how agents optimize workflows, automate quality control, and enhance advisory systems. Through real-world case studies, you will create agents capable of contextual reasoning, effective tool use, and seamless human collaboration. Finally, you’ll learn essential strategies for deployment, management, and ethical alignment, ensuring your AI solutions are both scalable and responsible in production environments.

Whether you're building your first intelligent agent or improving business systems, this book provides clear, actionable guidance for creating scalable and responsible AI solutions.

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What you will learn Deploy production-ready agent systems that scale securely and reliably Use LangChain and LangGraph to build autonomous agents with modular architectures Implement agents with sophisticated memory, planning, and reasoning capabilities Seamlessly integrate tools, APIs, and external data into agent workflows Establish robust evaluation frameworks to measure and optimize agent performance Implement guardrails and explainability features to ensure ethical and safe deployment Build multi-agent systems for complex, collaborative task orchestration Apply specific agent architectures across healthcare, finance, and legal domains Who this book is for This book is designed for AI engineers, software developers, machine learning researchers, and technical leaders who are building intelligent systems or deploying LLM-powered applications. It is particularly beneficial for professionals transitioning from traditional machine learning to agent-based architectures or those solving complex automation challenges. Python experience and basic machine learning knowledge are recommended to get the most out of the code implementations.

Table of Contents Foundations of Agent Engineering The Agent Engineer's Toolkit The Art of Agent Prompting Agent Deployment and Responsible Development Foundational Cognitive Architectures Information Retrieval and Knowledge Agents Tool Manipulation and Orchestration Agents Data Analysis and Reasoning Agents (N.B. Please use the Read Sample option to see further chapters)